Audience measurement of broadcasted television and/or radio programs has been practiced for many years. Audience measurement devices typically collect two kinds of information from households, namely, tuning information (e.g., information indicating the content presented to the audience such as channel information, time of consumption information, program information, etc.) and people information (e.g., information about the demographics of the audience). These two types of information are combined to produce meaningful ratings data.
People information has historically been gathered by people meters. People meters have been constructed in many different manners. For example, some people meters are active devices which seek to determine the composition of the audience by, for instance, analyzing visual images of the audience to actively determine the identity of the people in the audience. Such active determination involves comparing facial features of an individual appearing in a captured image to one or more previously stored facial feature images to search for a match. Other people meters are passive devices which prompt the members of the viewing audience to identify themselves by logging themselves in at specific times. These specific prompting times can be independent of the tuning information and at fixed time intervals (i.e., time-based prompting) or they can be tied to the tuning information and be performed, for example, when the channel changes (i.e., channel change-based prompting).
The time-based prompting technique poses a danger of under sampling or over sampling the data. For example, if the prompts are spaced too far apart in time, audience members may enter or leave the room between prompts. If the audience does not notify the people meter of such entrances/exits, audience composition data and audience change timing is lost. Alternatively, if the time prompts are spaced too closely in time, the audience members may become annoyed and/or reduce their compliance with the prompt requests. Again, audience composition data is lost in such circumstances.
The channel change-based prompting technique discussed above poses the danger of over sampling the data. As explained above, such overly frequent prompting may cause irritation and/or result in a decrease in compliance and a corresponding loss of data collection and/or invalid data.
It is also of interest to advertisers to know how many people are exposed to media, such as a particular sporting event, in public establishments such as a bar or a restaurant. Current methods include self-reporting by establishment owners and paid head-counters, which can be expensive, unreliable, and time-consuming.